Flair

Release 0.4.3 includes a host of new features including transformer-based embeddings (roBERTa, XLNet, XLM, etc.), fine-tuneable `FlairEmbeddings`, crosslingual MUSE embeddings, new data loading/sampling methods, speed/memory optimizations, bug fixes and enhancements. It also begins a refactoring of interfaces that prepares more general applicability of Flair to other types of downstream tasks.

Embeddings

Transformer embeddings (941 972 993)

Updates the old `pytorch-pretrained-BERT` library to the latest version of `pytorch-transformers` to support various new Transformer-based architectures for embeddings.

A total of 7 (new/updated) transformer-based embeddings can be used in Flair now:

python
from flair.embeddings import (
BertEmbeddings,
OpenAIGPTEmbeddings,
OpenAIGPT2Embeddings,
TransformerXLEmbeddings,
XLNetEmbeddings,
XLMEmbeddings,
RoBERTaEmbeddings,
)

bert_embeddings = BertEmbeddings()
gpt1_embeddings = OpenAIGPTEmbeddings()
gpt2_embeddings = OpenAIGPT2Embeddings()
txl_embeddings = TransformerXLEmbeddings()
xlnet_embeddings = XLNetEmbeddings()
xlm_embeddings = XLMEmbeddings()
roberta_embeddings = RoBERTaEmbeddings()


Detailed benchmarks on the downsampled CoNLL-2003 NER dataset for English can be found in 873 .

Crosslingual MUSE Embeddings (853)

Use the new `MuseCrosslingualEmbeddings` class to embed any sentence in one of 30 languages into the same embedding space. Behind the scenes the class first does language detection of the sentence to be embedded, and then embeds it with the appropriate language embeddings. If you train a classifier or sequence labeler with (only) this class, it will automatically work across all 30 languages, though quality may widely vary.

Here's how to embed:
python
initialize embeddings
embeddings = MuseCrosslingualEmbeddings()

two sentences in different languages
sentence_1 = Sentence("This red shoe is new .")
sentence_2 = Sentence("Dieser rote Schuh ist rot .")

language code is auto-detected
print(sentence_1.get_language_code())
print(sentence_2.get_language_code())

embed sentences
embeddings.embed([sentence_1, sentence_2])

print similarities
cos = torch.nn.CosineSimilarity(dim=0, eps=1e-6)
for token_1, token_2 in zip (sentence_1, sentence_2):
print(f"'{token_1.text}' and '{token_2.text}' similarity: {cos(token_1.embedding, token_2.embedding)}")



FastTextEmbeddings (879 )

Adds `FastTextEmbeddings` capable of handling for oov words. Be warned though that these embeddings are huge. `BytePairEmbeddings` are much smaller and reportedly of similar quality so it is probably advisable to use those instead.

Fine-tuneable FlairEmbeddings (922)

You can now fine-tune FlairEmbeddings on downstream tasks. You can **fine-tune an existing LM** by simply passing the `fine_tune` parameter in the `FlairEmbeddings` constructor, like this:

python
embeddings = FlairEmbeddings('news-foward', fine_tune=True)


You can also use this option to **task-train a wholly new language model** by passing an empty `LanguageModel` to the `FlairEmbeddings` constructor and the `fine_tune` parameter, like this:

python
make an empty language model
language_model = LanguageModel(
Dictionary.load('chars'),
is_forward_lm=True,
hidden_size=256,
nlayers=1)

init FlairEmbeddings to task-train this model
embeddings = FlairEmbeddings(language_model, fine_tune=True)



Optimizations

Automatic mixed precision support (934)

Mixed precision training can significantly speed up training. It can now be enabled by setting `use_amp=True` in the trainer classes. For instance for training language models you can do:

python
train your language model
trainer = LanguageModelTrainer(language_model, corpus)

trainer.train('resources/taggers/language_model',
sequence_length=256,
mini_batch_size=256,
max_epochs=10,
use_amp=True)


In our experiments, we saw 3x speedup of training large language models though results vary depending on your model size and experimental setup.

Control memory / speed tradeoff during training (891 809).

This release introduces the `embeddings_storage_mode` parameter to the `ModelTrainer` class and `predict()` methods. This parameter can be one of 'none', 'cpu' and 'gpu' and allows you to control the tradeoff between memory usage and speed during training:

- If set to '**none**' all embeddings are deleted after usage - this has lowest memory requirements but means that embeddings need to be recomputed at each epoch of training potentially causing a slowdown.
- If set to '**cpu**' all embeddings are moved to CPU memory after usage. During training, this means that they only need to be moved back to GPU for the forward pass, and not recomputed so in many cases this is faster, but requires memory.
- If set to '**gpu**' all embeddings stay on GPU memory after computation. This eliminates memory shuffling during training, causing a speedup. However this option requires enough GPU memory to be available for all embeddings of the dataset.

To use this option during training, simply set the parameter:

python
initialize trainer
trainer: ModelTrainer = ModelTrainer(tagger, corpus)
trainer.train(
"path/to/your/model",
embeddings_storage_mode='gpu',
)


This release also removes the `FlairEmbeddings`-specific disk-caching mechanism. In the future, a more general caching mechanism applicable to all embedding types may potentially be added as a fourth memory management option.

Speed-ups on in-memory datasets (792)

A new `DataLoader` abstract base class used in Flair will speed up data loading for in-memory datasets.


Refactoring of interfaces (891 843)

This release also slims down interfaces of `flair.nn.Model` and adds a new `DataPoint` interface that is currently implemented by the `Token` and `Sentence` classes. The idea is to widen the applicability of Flair to other data types and other tasks. In the future, the `DataPoint` interface will for example also be implemented by an `Image` object and new downstream tasks added to Flair.

The release also slims down the `evaluate()` method in the `flair.nn.Model` interface to take a `DataLoader` instead of a group of parameters. And refactors the logging header logic. Both refactorings prepare adding new new downstream tasks to Flair in the near future.

Other features

Training Classifiers with CSV files (826 952 967)

Adds the `CSVClassificationCorpus` so you can train classifiers directly from CSVs instead of first having to convert to FastText format. To load a CSV, you need to pass a `column_name_map` (like in `ColumnCorpus`), which indicates which column(s) in the CSV holds the text and which field(s) the label(s):

python
corpus = CSVClassificationCorpus(
path to the data folder containing train / test / dev files
data_folder='path/to/data',
indicates which columns are text and labels
column_name_map={4: "text", 1: "label_topic", 2: "label_subtopic"},
if CSV has a header, you can skip it
skip_header=True)


Data sampling (908)

We added the first (of many) data samplers that can be passed to the `ModelTrainer` to influence training. The `ImbalancedClassificationDatasetSampler` for instance will upsample rare classes and downsample common classes in a classification dataset. It may potentially help with imbalanced datasets. Call like this:
python
initialize trainer
trainer: ModelTrainer = ModelTrainer(tagger, corpus)
trainer.train(
'path/to/folder',
learning_rate=0.1,
mini_batch_size=32,
sampler=ImbalancedClassificationDatasetSampler,
)

There are two experimental chunk samplers (`ChunkSampler` and `ExpandingChunkSampler`) split a dataset into chunks and shuffle them. This preserves some ordering of the original data while also randomizing the data.


Visualization

- Adds HTML vizualization of sequence labeling (933). Call like this:
python
from flair.visual.ner_html import render_ner_html

tagger = SequenceTagger.load('ner')

sentence = Sentence(
"Thibaut Pinot's challenge ended on Friday due to injury, and then Julian Alaphilippe saw "
"his lead fall away. The BBC's Hugh Schofield in Paris reflects on 34 years of hurt."
)

tagger.predict(sentence)
html = render_ner_html(sentence)

with open("sentence.html", "w") as writer:
writer.write(html)


- Plotter now returns images for use in iPython notebooks (943)
- Initial TensorBoard support (924)
- Add pointer to Flair Visualizer (1014)

Additional parameterization options

- `CharacterEmbeddings` now let you specify number of hidden states and embedding size (834)
python
embedding = CharacterEmbedding(char_embedding_dim=64, hidden_size_char=64)

- Adds configuration option for minimal learning rate stopping criterion (871)
- `num_workers` is a parameter of `LanguageModelTrainer` (962 )

Bug fixes / enhancements
- Updates old pretrained models to remove old bugs / performance issues (1017)
- Fix error in RNN initialization in `DocumentRNNEmbeddings` (793)
- `ELMoEmbeddings` now use `flair.device` param (825)
- Fix download of TREC_6 dataset (896)
- Fix download of UD_GERMAN-HDT (980)
- Fix download of WikiNER_German (1006)
- Fix error in `ColumnCorpus` in which words that begin with hashtags were skipped as comments (956)
- Fix `max_tokens_per_do`c param in `ClassificationCorpus` (991)
- Simplify split rule in `ColumnCorpus` (990)
- Fix import error message for `ELMoEmbeddings` (1019)
- References to Persian language unified across embeddings (773)
- Updates most pre-trained models fixing quality issues / bugs (800)
- Clarifications in documentation (803 860 868)
- Fixes infinite loop for tokens without startpos (1030)

Enhancements
- Adds a learnable initial hidden state to `SequenceTagger` (899)
- Now keeps order of sentences in mini-batch when embedding (866)
- `SequenceTagger` now optionally returns a distribution of tag probabilities over all classes (782 949 1016)
- The model trainer now outputs a 'test.tsv' file that contains prediction of final model when done training (771 )
- Releases logging handler when finishing training a model (799)
- Fixes `bad_epochs` in training logs and no longer evaluates on test data at each epoch by default (818 )
- Convenience method to remove all empty sentences from a corpus (795)

New way of loading data (768)

The data loading part has been completely refactored to enable streaming data loading from disk using PyTorch's DataLoaders. I.e. training no longer requires the full dataset to be kept in memory, allowing us to train models over much larger datasets. This version also changes the syntax of how to load datasets.

Old way (now deprecated):
python
from flair.data_fetcher import NLPTaskDataFetcher, NLPTask
corpus = NLPTaskDataFetcher.load_corpus(NLPTask.UD_ENGLISH)


New way:
python
import flair.datasets
corpus = flair.datasets.UD_ENGLISH()


To use streaming loading, i.e. to not load into memory, you can pass the `in_memory` parameter:
python
import flair.datasets
corpus = flair.datasets.UD_ENGLISH(in_memory=False)


Embeddings

Flair embeddings (614)

This release brings Flair embeddings to 11 new languages (thanks stefan-it!): Arabic (ar), Danish (da), Persian (fa), Finnish (fi), Hebrew (he), Hindi (hi), Croatian (hr), Indonesian (id), Italian (it), Norwegian (no) and Swedish (sv). It also improves support for Bulgarian (bg), Czech, Basque (eu), Dutch (nl) and Slovenian (sl), and adds special language models for historical German. Load with language code, i.e.

python
load Flair embeddings for Italian
embeddings = FlairEmbeddings('it-forward')


One-hot encoded embeddings (747)

Some classification baselines work astonishingly well with simple learnable word embeddings. To support testing these baselines, we've added learnable word embeddings that start from a one-hot encoding of words. To initialize, you need to pass a corpus to initialize the vocabulary.

python
load corpus
import flair.datasets
corpus = flair.datasets.UD_ENGLISH()

init learnable word embeddings with corpus
embeddings = OneHotEmbeddings(corpus)


More options in `DocumentPoolEmbeddings` (747)

We now allow users to specify a fine-tuning option before the pooling operation is executed in document pool embeddings. Options are 'none' (no fine-tuning), 'linear' (linear remapping of word embeddings), 'nonlinear' (nonlinear remapping of word embeddings). Nonlinear should be used together with `WordEmbeddings`, while None should be used with `OneHotEmbeddings` (not necessary since they are already learnt on data). So, to replicate FastText classification you can either do:

python
instantiate one-hot encoded word embeddings
embeddings = OneHotEmbeddings(corpus)

document pool embeddings
document_embeddings = DocumentPoolEmbeddings([embeddings], fine_tune_mode='none')


or

python
instantiate pre-trained word embeddings
embeddings = WordEmbeddings('glove')

document pool embeddings
document_embeddings = DocumentPoolEmbeddings([embeddings], fine_tune_mode='nonlinear')


OpenAI GPT Embeddings (624)

We now support embeddings from the OpenAI GPT model. We use the excellent pytorch-pretrained-BERT library to download the GPT model, tokenize the input and extract embeddings from the subtokens.

Initialize with:

python
embeddings = OpenAIGPTEmbeddings()

Portuguese embeddings from NILC (576)

Extensibility to new downstream tasks (681)

Previously, we had the `SequenceTagger` and `TextClassifier` as the two downstream tasks supported by Flair. The `ModelTrainer` had specific methods to train these two models, making it difficult for users to add new types of tasks (such as text regression) to Flair.

This release refactors the `flair.nn.Model` and `ModelTrainer` functionality to make it uniform across tagging models and enable users to add new tasks to Flair. Now, by implementing the 5 methods in the `flair.nn.Model` interface, a custom model immediately becomes trainable with the `ModelTrainer`. Now, three types of downstream tasks implement this interface:

- the `SequenceTagger`,
- the `TextClassifier`
- and the beta `TextRegressor`.

The code refactor removes a lot of code redundancies and slims down the interfaces of the downstream tasks classes. As the sole breaking change, it removes the `load_from_file()` methods, which are now part of the `load()` method, i.e. if previously you loaded a self-trained model like this:

python
tagger = SequenceTagger.load_from_file('/path/to/model.pt')


You now do it like this:

python
tagger = SequenceTagger.load('/path/to/model.pt')


New features

- New beta support for text regression (564)
- Return confidence scores for single-label classification (664)
- Add method to find probability for each class in case of multi-class classification (693)
- Capability to change threshold during multi label classification 707
- Support for customized ELMo embeddings (661)
- Detect multi-label problems automatically: Previously, users always had to specify whether their text classification problem was multi_label or not. Now, this is detected automatically if users do not specify. So now you can init like this:

python
corpus
corpus = TREC_6()

make label_dictionary
label_dictionary = corpus.make_label_dictionary()

init text classifier
classifier = TextClassifier(document_embeddings, label_dictionary)


- We added better module descriptions to embeddings and dropout so that more parameters get printed by default for models for better logging. (747)
- Make 'cache_root' a global variable so that different directories can be chosen for caching (667)
- Both string and Token objects can now be passed to the add_token method (628)

New datasets
- Added IMDB classification corpus to `flair.datasets` (749)
- Added TREC_6 classification corpus to `flair.datasets` (749)
- Added 20 newsgroups classification corpus to `flair.datasets` (NEWSGROUPS object)
- WASSA-17 emotion intensity text regression tasks (695)

Bug fixes

- We normalized the training loss across modules so that train / test loss are consistent. (670)
- Permission error on Windows preventing model download (557)
- Handling of empty sentences (566 758)
- Fix text generation on CUDA (666)
- others ...

Updated documentation (https://github.com/zalandoresearch/flair/issues/138, https://github.com/zalandoresearch/flair/issues/89)
Expanded documentation and tutorials.

Release 0.4 with new models, lots of new languages, experimental multilingual models, hyperparameter selection methods, BERT and ELMo embeddings, etc.

New Features

Support for new languages

Flair embeddings
We now include new language models for:
* [Swedish](https://github.com/zalandoresearch/flair/issues/3)
* [Polish](https://github.com/zalandoresearch/flair/issues/187)
* [Bulgarian](https://github.com/zalandoresearch/flair/issues/188)
* [Slovenian](https://github.com/zalandoresearch/flair/issues/202)
* [Dutch](https://github.com/zalandoresearch/flair/issues/224)

In addition to English and German. You can load FlairEmbeddings for Dutch for instance with:

python
flair_embeddings = FlairEmbeddings('dutch-forward')


Word Embeddings

We now include pre-trained [FastText Embeddings for 30 languages](https://github.com/zalandoresearch/flair/issues/234): English, German, Dutch, Italian, French, Spanish, Swedish, Danish, Norwegian, Czech, Polish, Finnish, Bulgarian, Portuguese, Slovenian, Slovakian, Romanian, Serbian, Croatian, Catalan, Russian, Hindi, Arabic, Chinese, Japanese, Korean, Hebrew, Turkish, Persian, Indonesian.

Each language has embeddings trained over Wikipedia, or Web crawls. So instantiate with:

python
German embeddings computed over Wikipedia
german_wikipedia_embeddings = WordEmbeddings('de-wiki')

German embeddings computed over web crawls
german_crawl_embeddings = WordEmbeddings('de-crawl')


Named Entity Recognition

Thanks to the Flair community, we now include NER models for:
* [French](https://github.com/zalandoresearch/flair/issues/238)
* [Dutch](https://github.com/zalandoresearch/flair/issues/224)

Next to the previous models for English and German.

Part-of-Speech Taggigng

Thanks to the Flair community, we now include PoS models for:
* [German tweets](https://github.com/zalandoresearch/flair/issues/51)


Multilingual models

As a major new feature, we now include models that can tag text in various languages.

12-language Part-of-Speech Tagging

We include a PoS model trained over 12 different languages (English, German, Dutch, Italian, French, Spanish, Portuguese, Swedish, Norwegian, Danish, Finnish, Polish, Czech).

python
load model
tagger = SequenceTagger.load('pos-multi')

text with English and German sentences
sentence = Sentence('George Washington went to Washington . Dort kaufte er einen Hut .')

predict PoS tags
tagger.predict(sentence)

print sentence with predicted tags
print(sentence.to_tagged_string())


4-language Named Entity Recognition

We include a NER model trained over 4 different languages (English, German, Dutch, Spanish).

python
load model
tagger = SequenceTagger.load('ner-multi')

text with English and German sentences
sentence = Sentence('George Washington went to Washington . Dort traf er Thomas Jefferson .')

predict NER tags
tagger.predict(sentence)

print sentence with predicted tags
print(sentence.to_tagged_string())


This model also kind of works on other languages, such as French.

Pre-trained classification models ([issue 70](https://github.com/zalandoresearch/flair/issues/70))

Flair now also includes two pre-trained classification models:
* de-offensive-lanuage: detecting offensive language in German text ([GermEval 2018 Task 1](https://projects.fzai.h-da.de/iggsa/projekt/))
* en-sentiment: detecting postive and negative sentiment in English text ([IMDB](http://ai.stanford.edu/~amaas/data/sentiment/))

Simply load the `TextClassifier` using the preferred model, such as
python
TextClassifier.load('en-sentiment')


BERT and ELMo embeddings

We added both BERT and ELMo embeddings so you can try them out, and mix and match them with Flair embeddings or any other embedding types. We hope this will enable the research community to better compare and combine approaches.

BERT Embeddings ([issue 251](https://github.com/zalandoresearch/flair/issues/251))

We added [BERT embeddings](https://arxiv.org/pdf/1810.04805.pdf) to Flair. We are using the implementation of [huggingface](https://github.com/huggingface/pytorch-pretrained-BERT). The embeddings can be used as any other embedding type in Flair:

python
from flair.embeddings import BertEmbeddings
init embedding
embedding = BertEmbeddings()
create a sentence
sentence = Sentence('The grass is green .')
embed words in sentence
embedding.embed(sentence)


ELMo Embeddings ([issue 260](https://github.com/zalandoresearch/flair/issues/260))

Flair now also includes [ELMo embeddings](http://www.aclweb.org/anthology/N18-1202). We use the implementation of [AllenNLP](https://allennlp.org/elmo). As this implementation comes with a lot of sub-dependencies, you need to first install the library via `pip install allennlp` before you can use it in Flair. Using the embeddings is as simple as using any other embedding type:
python
from flair.embeddings import ELMoEmbeddings
init embedding
embedding = ELMoEmbeddings()
create a sentence
sentence = Sentence('The grass is green .')
embed words in sentence
embedding.embed(sentence)



Multi-Dataset Training ([issue 232](https://github.com/zalandoresearch/flair/issues/232))

You can now train a model on on multiple datasets with the `MultiCorpus` object. We use this to train our multilingual models.

Just create multiple corpora and put them into `MultiCorpus`:

python
english_corpus = NLPTaskDataFetcher.load_corpus(NLPTask.UD_ENGLISH)
german_corpus = NLPTaskDataFetcher.load_corpus(NLPTask.UD_GERMAN)
dutch_corpus = NLPTaskDataFetcher.load_corpus(NLPTask.UD_DUTCH)

multi_corpus = MultiCorpus([english_corpus, german_corpus, dutch_corpus])

The `multi_corpus` can now be used for training, just as any other corpus before. Check [the tutorial](TUTORIAL_6_TRAINING_A_MODEL.md) for more details.

Parameter Selection using Hyperopt ([issue 242](https://github.com/zalandoresearch/flair/issues/242))

We built a wrapper around [hyperopt](http://hyperopt.github.io/hyperopt/) to allow you to search for the best hyperparameters for your downstream task.

Define your search space and start training using several different parameter settings. The results are written to a specific file called `param_selection.txt` in the result directory. Check [the tutorial](TUTORIAL_7_HYPER_PARAMETER.md) for more details.

NLP Dataset Downloader ([issue 243](https://github.com/zalandoresearch/flair/issues/243))

To make it as easy as possible to start training models, we have a new feature for automatically downloading publicly available NLP datasets. For instance, by running this code:

python
corpus = NLPTaskDataFetcher.load_corpus(NLPTask.UD_ENGLISH)


you download the Universal Dependencies corpus for English and can immediately start training models. The list of available datasets can be found in [the tutorial](TUTORIAL_5_CORPUS.md).


Model training features

We added various other features to model training.

Saving training log ([issue 212](https://github.com/zalandoresearch/flair/issues/212))

The training log output will from now on be automatically saved in the result directory you provide for training.
The log will be saved in `training.log`.

Resuming training ([issue 217](https://github.com/zalandoresearch/flair/issues/217))

It is now possible to stop training at any point in time and to resume it later by training with `checkpoint` set to `True`. Check [the tutorial](TUTORIAL_6_TRAINING_A_MODEL.md) for more details.

Custom Optimizers ([issue 220](https://github.com/zalandoresearch/flair/issues/220))

You can now choose other optimizers besides SGD, i.e. any PyTorch optimizer, plus our own modified implementations of SDG and Adam, namely SGDW and AdamW.

Learning Rate Finder ([issue 228](https://github.com/zalandoresearch/flair/issues/228))

A new helper method to assist you in finding a [good learning rate for model training](https://github.com/zalandoresearch/flair/blob/master/resources/docs/TUTORIAL_8_MODEL_OPTIMIZATION.md#finding-the-best-learning-rate).


Breaking Changes

This release introduces breaking changes. The most important are:

Unified Model Trainer ([issue 189](https://github.com/zalandoresearch/flair/issues/189))

Instead of maintaining two separate trainer classes for sequence labeling and text classification, we now have one model training class, namely `ModelTrainer`. This replaces the earlier classes `SequenceTaggerTrainer` and `TextClassifierTrainer`.

Downstream task models now implement the new `flair.nn.Model` interface. So, both the `SequenceTagger` and `TextClassifier` now inherit from `flair.nn.Model`. This allows both models to be trained with the `ModelTrainer`, like this:

python
Training text classifier
tagger = SequenceTagger(512, embeddings, tag_dictionary, 'ner')
trainer = ModelTrainer(tagger, corpus)
trainer.train('results')

Training text classifier
classifier = TextClassifier(document_embedding, label_dictionary=label_dict)
trainer = ModelTrainer(classifier, corpus)
trainer.train('results')


The advantage is that all training parameters ans training procedures are now the same for sequence labeling and text classification, which reduces redundancy and hopefully make it easier to understand.

Metric class

The metric class is now refactored to compute micro and macro averages for F1 and accuracy. There is also a new enum `EvaluationMetric` which you can pass to the ModelTrainer to tell it what to use for evaluation.

Updates and Bug Fixes

Torch 1.0 ([issue 176](https://github.com/zalandoresearch/flair/issues/299))

Flair now bulids on torch 1.0.

Use Pathlib ([issue 176](https://github.com/zalandoresearch/flair/issues/176))

Flair now uses `Path` wherever possible to allow easier operations on files/directories. However, our interfaces still allows you to pass a string, which will then be transformed into a Path by Flair.

Bug Fixes

* Fix: Non-whitespaced tokenized text results into an infinite loop ([issue 226](https://github.com/zalandoresearch/flair/issues/226))
* Fix: Getting IndexError: list index out of range error ([issue 233](https://github.com/zalandoresearch/flair/issues/233))
* Do not reset cache directory always to None ([issue 249](https://github.com/zalandoresearch/flair/issues/249))
* Filter sentences with zero tokens ([issue 266](https://github.com/zalandoresearch/flair/issues/266))

This is an update over release 0.3.1 with some critical bug fixes, a few new features and a lot more pre-packaged embeddings.

New Features

Embeddings

More word embeddings (194 )

We added FastText embeddings for 10 languages ('en', 'de', 'fr', 'pl', 'it', 'es', 'pt', 'nl', 'ar', 'sv'), load using the two-letter language code, like this:

python
french_embedding = WordEmbeddings('fr')


More character LM embeddings (204 187 )

Thanks to contribution by [stefan-it](https://github.com/stefan-it/flair-lms), we added CharLMEmbeddings for Bulgarian and Slovenian. Load like this:

python
flm_embeddings = CharLMEmbeddings('slovenian-forward')
blm_embeddings = CharLMEmbeddings('slovenian-backward')


Custom embeddings (170 )

Add explanation on how to use your own custom word embeddings. Simply convert to gensim.KeyedVectors and point embedding class there:

python
custom_embedding = WordEmbeddings('path/to/your/custom/embeddings.gensim')


New embeddings type: `DocumentPoolEmbeddings` (191 )

Add a new embedding class for document-level embeddings. You can now choose between different pooling options, e.g. min, max and average. Create the new embeddings like this:

python
word_embeddings = WordEmbeddings('glove')
pool_embeddings = DocumentPoolEmbeddings([word_embeddings], mode='min')


Language model

New method: `generate_text()` (167 )

The `LanguageModel` class now has an in-built `generate_text()` method to sample the LM. Run code like this:

python
load your language model
model = LanguageModel.load_language_model('path/to/your/lm')

generate 2000 characters
text = model.generate_text(20000)
print(text)


Metrics

Class-based metrics in `Metric` class (164 )

Refactored Metric class to provide class-based metrics, as well as micro and macro averaged F1 scores.

Bug Fixes

Fix serialization error for MacOS and Windows (174 )

On these setups, we got errors when serializing or loading large models. We've put in place a workaround that limits model size so it works on those systems. Added bonus is that models are smaller now.

"Frozen" dropout (184 )

Potentially big issue in which dropout was frozen in the first epoch in embeddings produced from the character LM, meaning that throughout training the same dimensions stayed dropped. Fixed this.

Testing step in language model trainer (178 )

Previously, the language model was never applied to test data during training. A final testing step has been added in (again).

Testing

Distinguish between unit and integration tests (183)

Instructions on how to run tests with pipenv (161 )

Optimizations

Disable autograd during testing and prediction (175)

Since autograd is unused here this gives us minor speedups.

This is a stability-update over release 0.3.0 with small optimizations, refactorings and bug fixes. For list of new features, refer to 0.3.0.

Optimizations

Retain Token embeddings in memory by default (146 )

Allow for faster training of text classifier on large datasets by keeping token embeddings im memory.

Always clear embeddings after prediction (149 )

After prediction, remove embeddings from memory to avoid filling up memory.


Refactorings

Alignd TextClassificationTrainer and SquenceTaggerTrainer (148 )

Align signatures and features of the two training classes to make it easier to understand training options.

Updated DocumentLSTMEmbeddings (150 )

Remove unused flag and code from DocumentLSTMEmbeddings

Removed unneeded AWS and Jinja2 dependencies (158 )

Some dependencies are no longer required.


Bug Fixes

Fixed error when predicting over empty sentences. (157)

Serialization: reset cache settings when saving a model. (153 )